Tag: mobility

Accomplishing a transition from the current state of the economy to an inclusive green growth path is one of the global challenges society is faced with.

This involves transitions in many sectors – the Green Growth pilot of CoeGSS considers the case of sustainable mobility. It investigates the diffusion of electric vehicles in the global car fleet to gain an understanding of mechanisms that may foster or hinder a sustainability transition and to explore potential evolutions of the global car fleet with their implications for inclusive green growth.

The transport sector is responsible for around one quarter of Europe’s greenhouse gas (GHG) emissions, contributing to climate change. Emissions from road vehicles also contribute to high concentrations of air pollutants in many of Europe’s cities. Further, road transport is the main source of environmental noise pollution in Europe, harming human health and well-being. (ref: European Envinronment Agency – 2017).

To assess possible socio-technical transition pathways towards sustainable mobility, the Mobility Transition Model (MoTMo) has been developed. It is applied to Germany as an example country. MoTMo is an agent-based model representing individuals and households that choose from a list of mobility modes (such as conventional cars, electric vehicles, or public transport or car sharing) based on opinions about these and on the priorities they assign to convenience, emissions, costs, and being innovative. Opinions evolve according to experience and influence from others in a social contact network. A synthetic population of 10 Mio individuals with mobility profiles, that statistically matches the population of Germany for relevant features, has been developed as a starting point for model simulations.

In a first phase, technological and social innovations (here, electric vehicles and car sharing) are generally used by very small numbers of people. Also, innovations often develop in niches (e.g., spatial or social groups) first. Therefore, several technical challenges are encountered in modelling the diffusion of innovations: a large number of agents and a high resolution of each model simulation run are needed to statistically capture small proportions of the population and to represent niches. Many simulation runs and thorough analysis of their output data are then needed to explore potential evolutions of the system. This required a parallelized MoTMo and large computational resources.

Figure 1 shows the spatial distribution of electric vehicles in the Niedersachsen, Bremen, and Hamburg area obtained from simulations. The four maps depict the development over time that highlights how electric mobility first develops within cities and expand to the surrounding rural areas in later stages. Dominant focal points are the cities of Hamburg, Bremen and Braunschweig, whereas in Hannover, the very dense population favours the use of other (public) mobility.

Figure 1: Spatial distribution of green cars in the Niedersachsen, Bremen, and Hamburg area

The work is carried out in the context of enhancing GSS modelling through High Performance Computing (HPC) and Data Analytics (HPDA), by enabling the use of high-resolution data sets, by allowing models to grow in complexity and grow towards global scales, and by facilitating deeper analysis of larger sets of output data from model simulation runs.

The Centre of Excellence for Global Systems Science provides help on all of the above: generating synthetic populations, generating interaction networks between agents in a synthetic population, technical support for parallelizing an agent-based model and the computing infrastructure for running it, as well as data analytics and visualization of results.

Figure 2: Comparing two scenarios for charging stations deployment. On the left side, a linear increase is assumed, on the right side the increase is exponential till 2030. The heights of the spikes show the number of charging stations in 2035, the color indicates the demand for electric vehicles (the brighter, the more electric vehicles).